itsCausal: Interrupted Time Series in real world dataThe User’s Guide
2024-05-11
Solution: interrupted time series (ITS)
Challenge: Standard interrupted time series (ITS) may produce biased estimates because of poor prediction, poor forecasting, and simplification assumptions with panel data.
“The risk of bias for ITS studies was high for 53.3% and very high for 19.2%”. (Hategeka et al. (2020), N = 120, 1990-2020)
itsCausal was born from the practical need for effective healthcare monitoringitsCausal”, we aim to deliver a set of recommendations for practitionersitsCausal”, which
itsCausalWe benchmark our method with experimental evidence
itsCausal is within the 95% CI of the ATE from the RCTitsCausal: A powerful tool for estimating causal effects without a control group.The following assumptions must hold (see Cerqua, Letta, and Menchetti (2024)):